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I use the popular K-Means clustering algorithm to segment customers based on their response to a series of marketing campaigns
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README.md

Using K-Means Clustering to Understand Marketing Response

Market segmentation refers to the process of dividing a consumer market of existing and/or potential customers into groups (or segments) based on shared attributes, interests, and behaviours.

For this mini-project I will use the popular K-Means clustering algorithm to segment customers based on their response to a series of marketing campaigns. The basic concept is that consumers who share common traits would respond to marketing communication in a similar way so that companies can reach out for each group in a relevant and effective way.

With little effort we will leaarn that some of our customers favour certain varieties of wine whereas others prefer to buy high or low quantities. Such information can be used to tailor your pricing strategies and marketing campaings towards those customers that are more inclined to respond. Moreover, customer segmentation allows for a more efficient allocation of marketing resources and the maximization of cross- and up-selling opportunities.

Although it’s not going to give you all the answers, clustering is a powerful exploratory exercise that can help you reveal patterns in your consumer base, especially when you have a brand new market to explore and do not have any prior knowledge of it. It’s very easy to implement and you can unearth interesting patterns of behaviour in your customer base.

Links

You can find the final article on my website

I've also published the article on Towards Data Science

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